2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) April 09-11. 2021, ISTTS Surabaya, Indonesia 978-1-6654-0514-0/21/$31.00 ©2021 IEEE 61 Application of Deep Learning for Early Detection of COVID-19 Using CT-Scan Images 1 st Judith Chrisolita Sangidong Dept. of Information Technology Universitas Kristen Satya Wacana Salatiga, Indonesia jchrisolita@gmail.com 2 nd Hindriyanto Dwi Purnomo Dept. of Information Technology Universitas Kristen Satya Wacana Salatiga, Indonesia hindriyanto.purnomo@uksw.edu 3 rd Fian Yulio Santoso Dept. of Information Technology Universitas Kristen Satya Wacana Salatiga, Indonesia fianyuliosantoso@gmail.coml Abstract—COVID-19 pandemic caused a vast impact worldwide. The imbalance between the number of tools for COVID-19 detection and the demand for COVID-19 tests from citizens has overwhelmed the government. To overcome this problem, artificial intelligence is utilized, specifically in the deep learning field. In this paper, we propose FJCovNet, a new deep learning model based on DenseNet121. FJCovNet managed to get an accuracy of 98.14%, surpassing Xception with an accuracy of 84,24%, VGG19 with an accuracy of 95.25%, and ResNet50 with accuracy of 91.53%. FJCovNet also managed to get less training time with 612 seconds, lesser than VGG19 with 808 seconds and ResNet50 with 809 seconds, and only slightly more than Xception with 609 seconds. Keywords—COVID-19, CT-Scan, deep learning, pretrained model. I. INTRODUCTION Coronavirus Disease (COVID-19) is an infectious disease caused by the new coronavirus (Sars-CoV-2) which was found in the city of Wuhan, Hubei Province, China, in 2019 and spread throughout the world in 2020, resulting in COVID-19 being declared as a Pandemic by WHO on March 11, 2020 out [1]. The unavailability of a vaccine for COVID-19 causes the number of COVID-19 cases to continue to grow. As of March 3 rd 2021 10.40 WIB, cases of COVID-19 worldwide reached 21,623,570 positive patients, 91,115,487 patients recovered, and 2,560,602 patients died [2]. The impact of COVID-19 is life-threatening and results in a decline in all aspects of life. In the field of education, teaching and learning activities are carried out online. Students in remote areas experience difficulties due to limited information technology infrastructure [3]. In the health sector, there is a high demand for medical personnel and/or doctors capable of dealing with COVID-19. Lack of PPE (Personal Protective Equipment) supplies, heavy workloads, and stress due to not being able to meet family and anxiety about contracting COVID-19 have increased the number of medical personnel and/or exhausted doctors who contracted COVID- 19 and even died [3], [4]. On the economic front, COVID-19 has caused most countries to face a recession in 2020 [5]. If prevention is not carried out, the country can experience poverty, which has a major impact on its welfare. To maintain the welfare of the state and society, the government raises funds to help referral hospitals for COVID- 19 and monitor individuals who have the potential to have COVID-19 to minimize the spread of the virus. Therefore, large funds are needed. The country's economy must keep running. To ensure that the economy continues without increasing the number of COVID-19 cases, the New Normal concept is implemented to carry out normal activities by implementing health protocols to prevent transmission of COVID-19 [6]. An important step needed to minimize the spread of the virus and implement the New Normal is a test to detect whether someone has COVID-19. If an individual is positive for COVID-19, then that individual must be isolated and undergo treatment and not do activities. The test widely used to confirm COVID-19 and trusted to be accurate is the reverse-transcription polymerase chain reaction (RT-PCR). However, there are times when the sensitivity of RT-PCR is not very high for early detection and treatment in suspected patients [7], [8]. This can increase the risk that individuals who are positive for COVID-19 can continue their activities and endanger the people they meet. Besides, the diagnosis of a suspect patient requires several time-consuming tests [8]. The number of suspect patients is always increasing, and the number of test kits in hospitals is relatively limited. [9]. These things can hinder the process of preventing COVID-19. II. LITERATURE STUDY Artificial Intelligence (AI) is studying how to program machines or computers to do things humans do [10]. Along with the development of the times, artificial intelligence technology is increasingly sophisticated and is increasingly being used because of its ability to do human work to increase efficiency in everyday work. Artificial intelligence can be developed into various types of fields of science. One of the applications of artificial intelligence that has been developed is in the field of Computer Vision. Computer Vision is a branch of Artificial Intelligence that studies and empowers machines to function like the human eye [11]. Computer Vision has been applied to various fields of science. One of the studies in agriculture uses computer vision to determine the quality of tomatoes [11]. Meanwhile, in the health sector, Computer Vision is widely applied. Research conducted by P Vávra et al. demonstrated an increasing interest in surgeons to use AR in surgery as it increases the safety and effectiveness of surgical procedures [12]. In a study conducted by Shivangi Jain et al., Image processing was applied to detect melanoma skin cancer [12], [13]. Computer vision is also applied in the early detection of breast cancer [14] and early detection of pneumonia [15]. The application of computer vision in these studies is made with deep learning, which is a type of artificial intelligence that uses an algorithm or set of mathematical instructions inspired by how the human brain works [16]. Deep learning has shown excellent performance in solving computer vision challenges, such as 2021 3rd East Indonesia Conference on Computer and Information Technology (EIConCIT) | 978-1-6654-0514-0/20/$31.00 ©2021 IEEE | DOI: 10.1109/EIConCIT50028.2021.9431887